How We Prepare Documents for LLMs

Language model quality starts long before the first prompt — with how the documents it runs on are prepared. Here are the main steps of our process.

1. Corpus collection and cleaning

We start by gathering the source documents and bringing them to a single form: text extraction from PDF/DOCX/HTML, removal of boilerplate junk (headers/footers, navigation, duplicates), normalization of encoding and markup.

2. Structuring for RAG

Next, documents are structured for retrieval-augmented generation:

• Chunking — splitting into meaningful fragments, not a fixed number of tokens "blindly".
• Metadata — source, section, date, document type: this is what retrieval is later filtered and ranked by.
• Retrieval verification — a sample run of queries to make sure the needed fragments are actually found.

3. Formats for fine-tuning

If the data is being prepared for fine-tuning, we convert it to the target format (instruction/response, dialogs), validate examples for consistency and split the dataset into leak-free train/val/test.

4. Quality control

The final step is an audit: deduplication, completeness metrics, selective manual review. Only then does the data go to the client along with process documentation.

Got a corpus that needs tidying up? Write to us: info@ai-engineering.space.